Detecting Anomalies in Firewall Logs Using Artificially Generated Attacks
Author:
Affiliation:
1. University of Zagreb,Laboratory for Information Security and Privacy, Faculty of Electrical Engineering and Computing,Zagreb,Croatia
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10198868/10198885/10198912.pdf?arnumber=10198912
Reference44 articles.
1. Using data mining for discovering anomalies from firewall logs: a comprehensive review;as-suhbani;International Research Journal of Engineering and Technology (IRJET),2017
2. Discovering cluster-based local outliers
3. Discovering anomalous rules in firewall logs using data mining and machine learning classifiers;khamitkar;International Journal of Scientific & Technology Research,2020
4. Outlier detection in the multiple cluster setting using the minimum covariance determinant estimator
5. Unsupervised One-Class Learning for Anomaly Detection on Home IoT Network Devices
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Comparative Analysis of Anomaly Detection Approaches in Firewall Logs: Integrating Light-Weight Synthesis of Security Logs and Artificially Generated Attack Detection;Sensors;2024-04-20
2. An Optimized Approach for Assisted Firewall Anomaly Resolution;IEEE Access;2023
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